The paper introduces MedGym, a benchmark environment for dynamic medical treatment recommendation that models patient evolution in continuous time using Physics-Informed Neural Networks. It addresses gaps in existing reinforcement learning (RL) approaches by allowing evaluation of RL methods under irregular measurement intervals, personalized treatment responses, and safety considerations. MedGym enables direct comparison between discrete-time and continuous-time RL methods and supports clinically relevant metrics such as personalization and trajectory-level safety.
By Yuepeng Wang, Ken Kawano, Yoshihiko Fujisawa, Yongqi Zhou, Akifumi Wachi, Mehrshad Sadria, Lei Zhou, Richard Weiss, Katsuki Fujisawa, Ying Chen, Xin Liu, Kyoung-Sook Kim, Xiao Hu, Sebastien Gros, Xun Shen
arXiv:2606. 19481v1 Announce Type: new Abstract: Offline reinforcement learning (ORL) offers the potential to improve the quality of clinical decision-making using historical electronic health record (EHR) data.
By Thomas Frost, Steve Harris
arXiv:2606. 01051v1 Announce Type: new Abstract: Dynamic medical treatment requires deciding treatment intensity and intervention timing, while patient states evolve continuously and adverse events may occur between clinical interactions.
By Xun Shen, Yuepeng Wang, Akifumi Wachi, Yongqi Zhou, Richard Weiss, Yoshihiko Fujisawa, Ken Kawano, Mehrshad Sadria, Ying Chen, Xin Liu, Sebastien Gros, Xiao Hu, Kyoung-Sook Kim, Mengmou Li, Katsuki Fujisawa, Kenji Wakabayashi
arXiv:2606. 17405v1 Announce Type: new Abstract: Clinical decision support AI systems (CDSASs) must adapt to evolving patient conditions in real-time while adhering to strict safety constraints.
By Xinyu Qin, Anil K. Sood, Ruiheng Yu, Sara Corvigno, Elaine Stur, Lu Wang
arXiv:2512. 08029v3 Announce Type: replace Abstract: Clinical decision-making in oncology requires predicting dynamic disease evolution, a task current static AI predictors cannot perform.
By Tianxingjian Ding, Yuanhao Zou, Chen Chen, Mubarak Shah, Yu Tian
arXiv:2607. 16916v1 Announce Type: new Abstract: Bladder cancer treatment requires personalized and adaptive decision-making, particularly for recurrent disease, where treatment effectiveness changes across successive clinical episodes.
By Divyansh Chawla, Anshu Garg, Isshaan Singh